diff --git a/src/torchio/transforms/augmentation/composition.py b/src/torchio/transforms/augmentation/composition.py
index 2e8b7b3c..c09aa856 100644
--- a/src/torchio/transforms/augmentation/composition.py
+++ b/src/torchio/transforms/augmentation/composition.py
@@ -2,6 +2,8 @@
 
 import warnings
 from collections.abc import Sequence
+from typing import Any
+from typing import TypeAlias
 from typing import Union
 
 import numpy as np
@@ -11,7 +13,9 @@
 from ..transform import Transform
 from . import RandomTransform
 
-TypeTransformsDict = Union[dict[Transform, float], Sequence[Transform]]
+TypeTransformsDict: TypeAlias = Union[dict[Transform, float], Sequence[Transform]]
+HydraConfig: TypeAlias = dict[str, Any]
+HydraConfigDict: TypeAlias = dict[str, HydraConfig]
 
 
 class Compose(Transform):
@@ -81,6 +85,15 @@ def inverse(self, warn: bool = True) -> Compose:
             )
         return result
 
+    def to_hydra_config(self) -> HydraConfig:
+        """Return a dictionary representation of the transform for Hydra instantiation."""
+        transform_dict: HydraConfig = {'_target_': self._get_name_with_module()}
+        transform_dict['transforms'] = []
+        transform_dict.update(self._get_reproducing_arguments())
+        for transform in self.transforms:
+            transform_dict['transforms'].append(transform.to_hydra_config())
+        return self._tuples_to_lists(transform_dict)
+
 
 class OneOf(RandomTransform):
     """Apply only one of the given transforms.
diff --git a/src/torchio/transforms/transform.py b/src/torchio/transforms/transform.py
index 2375e7ce..26735d09 100644
--- a/src/torchio/transforms/transform.py
+++ b/src/torchio/transforms/transform.py
@@ -596,3 +596,22 @@ def get_mask_from_bounds(
         mask = torch.zeros_like(tensor, dtype=torch.bool)
         mask[:, i0:i1, j0:j1, k0:k1] = True
         return mask
+
+    def _get_name_with_module(self) -> str:
+        """Return the name of the transform including its module."""
+        return f'{self.__class__.__module__}.{self.__class__.__name__}'
+
+    @staticmethod
+    def _tuples_to_lists(obj):
+        if isinstance(obj, (tuple, list)):
+            return [Transform._tuples_to_lists(x) for x in obj]
+        if isinstance(obj, dict):
+            return {k: Transform._tuples_to_lists(v) for k, v in obj.items()}
+        return obj
+
+    def to_hydra_config(self) -> dict:
+        """Return a dictionary representation of the transform for Hydra instantiation."""
+        target = self._get_name_with_module()
+        transform_dict = {'_target_': target}
+        transform_dict.update(self._get_reproducing_arguments())
+        return self._tuples_to_lists(transform_dict)
diff --git a/src/torchio/visualization.py b/src/torchio/visualization.py
index f177862a..dc260c8e 100644
--- a/src/torchio/visualization.py
+++ b/src/torchio/visualization.py
@@ -103,6 +103,14 @@ def plot_volume(
     elif rgb and image.num_channels == 3:
         data = image.data  # keep image as it is
     elif channel is None:
+        if image.num_channels > 1:
+            message = (
+                'Multiple channels found in the image. '
+                'Plotting the first channel (0). '
+                'To plot a different channel, please specify the channel '
+                'index using the "channel" argument.'
+            )
+            warnings.warn(message, RuntimeWarning, stacklevel=2)
         data = image.data[0:1]  # just use the first channel
     else:
         data = image.data[np.newaxis, channel]